76,460 research outputs found
Link Count Data-driven Static Traffic Assignment Models Through Network Modularity Partitioning
Accurate static traffic assignment models are important tools for the
assessment of strategic transportation policies. In this article we present a
novel approach to partition road networks through network modularity to produce
data-driven static traffic assignment models from loop detector data on large
road systems. The use of partitioning allows the estimation of the key model
input of Origin-Destination demand matrices from flow counts alone. Previous
network tomography-based demand estimation techniques have been limited by the
network size. The amount of partitioning changes the Origin-Destination
estimation optimisation problems to different levels of computational
difficulty. Different approaches to utilising the partitioning were tested, one
which degenerated the road network to the scale of the partitions and others
which left the network intact. Applied to a subnetwork of England's Strategic
Road Network and other test networks, our results for the degenerate case
showed flow and travel time errors are reasonable with a small amount of
degeneration. The results for the non-degenerate cases showed that similar
errors in model prediction with lower computation requirements can be obtained
when using large partitions compared with the non-partitioned case. This work
could be used to improve the effectiveness of national road systems planning
and infrastructure models.Comment: 29 pages, 11 figure
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Traffic engineering in ambient networks: challenges and approaches
The focus of this paper is on traffic engineering in ambient networks.
We describe and categorize different alternatives for making the routing more adaptive to the current traffic situation and discuss the challenges that ambient networks pose on traffic engineering methods. One of the main objectives of traffic engineering is to avoid congestion by controlling and optimising the routing function, or in short, to put the traffic where the capacity is. The main challenge for traffic engineering in ambient networks is to cope with the dynamics of both topology and traffic demands. Mechanisms are needed that can handle traffic load dynamics in scenarios with sudden changes in traffic demand and dynamically distribute traffic to benefit from available resources. Trade-offs between optimality, stability and signaling overhead that are important for traffic engineering methods in the fixed Internet becomes even more critical in a dynamic ambient environment
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